Method of determining PSF using multiple instances of a nominally similar scene

ABSTRACT

A digital image acquisition system includes a portable apparatus for capturing digital images and a digital processing component for detecting, analyzing and informing the photographer regarding motion blur, and for reducing camera motion blur in an image captured by the apparatus. The digital processing component operates by comparing the image with at least one other image, for example a preview image, of nominally the same scene taken outside the exposure period of the main image. In one embodiment the digital processing component identifies at least one feature in a single preview image which is relatively less blurred than the corresponding feature in the main image, calculates a point spread function (PSF) in respect of such feature, and de-convolves the main image using the PSF. In another embodiment, the digital processing component calculates a trajectory of at least one feature in a plurality of preview images, extrapolates such feature on to the main image, calculates a PSF in respect of the feature, and de-convolves the main image using the PSF. In another embodiment the digital processing unit after determining the degree of blur notifies the photographer of the existing blur or automatically invokes consecutive captures.

RELATED APPLICATIONS

This application is a Division of U.S. patent application Ser. No.12/702,092, filed Feb. 8, 2010, which is a Continuation of U.S. patentapplication Ser. No. 11/566,180, filed Dec. 1, 2006, now U.S. Pat. No.7,660,478, which is a Continuation of U.S. patent application Ser. No.10/985,657, filed Nov. 10, 2004, now U.S. Pat. No. 7,636,486, which ishereby incorporated by reference.

FIELD OF THE INVENTION

This invention relates to a digital image acquisition system comprisinga digital processing component for determining a camera motion blurfunction in a captured digital image.

BACKGROUND TO THE INVENTION

Camera motion is dependent on a few parameters. First of all, theexposure speed. The longer the shutter is open, the more likely thatmovement will be noticed. The second is the focal length of the camera.The longer the lens is, the more noticeable the movement is. A rule ofthumb for amateur photographers shooting 35 mm film is never to exceedthe exposure time beyond the focal length, so that for a 30 mm lens, notto shoot slower than 1/30th of a second. The third criteria is thesubject itself. Flat areas, or low frequency data, is less likely to bedegraded as much as high frequency data.

Historically, the problem was addressed by anchoring the camera, such aswith the use of a tripod or monopod, or stabilizing it such as with theuse of gyroscopic stabilizers in the lens or camera body, or movement ofthe sensor plane to counteract the camera movement.

Mathematically, the motion blurring can be explained as applying a PointSpread Function, or PSF, to each point in the object. This PSF representthe path of the camera, during the exposure integration time. Motion PSFis a function of the motion path and the motion speed, which determinesthe integration time, or the accumulated energy for each point.

A hypothetical example of such a PSF is illustrated in FIG. 3-a and 3-b.FIG. 3-b is a projection of FIG. 3-a. In FIGS. 3-a and 3-b, the PSF isdepicted by 410 and 442 respectively. The pixel displacement in x and ydirections are depicted by blocks 420 and 421 respectively for the Xaxis and 430 and 432 for the Y axis respectively. The energy 440 is thethird dimension of FIG. 3-a. Note that the energy is the inverse of thedifferential speed in each point, or directly proportional to the timein each point. In other words, the longer the camera is stationary at agiven location, the longer the integration time is, and thus the higherthe energy packed. This may also be depicted as the width of the curve442 in a X-Y projection.

Visually, when referring to images, in a simplified manner, FIG. 3-cillustrates what would happen to a pinpoint white point in an imageblurred by the PSF of the aforementioned Figures. In a picture, suchpoint of light surrounded by black background will result in an imagesimilar to the one of FIG. 3-c. In such image, the regions that thecamera was stationary longer, such as 444 will be brighter than theregion where the camera was stationary only a fraction of that time.Thus such image may provide a visual speedometer, or visualaccelerometer. Moreover, in a synthetic photographic environment suchknowledge of a single point, also referred to as a delta-function coulddefine the PSF.

Given:

-   -   a two dimensional image I represented by I(x,y)    -   a motion point spread function MPSF(I)    -   The degraded image I′(x,y) can be mathematically defined as the        convolution of I(X,Y) and MPSF(x,y) or        I′(x,y)=I(x,y)        MPSF(x,y)  (Eq. 1)

or in the integral form for a continuous functionI(x,y)=∫∫(I(x−x′,y−y′)MPSF(x′y′)∂x′∂y′  (Eq. 2)and for a discrete function such as digitized images:

$\begin{matrix}{{I^{\prime}\left( {m,n} \right)} = {\sum\limits_{j}{\sum\limits_{k}{{I\left( {{m - j},{n - k}} \right)}{{MPSF}\left( {j,k} \right)}}}}} & \left( {{Eq}.\mspace{14mu} 3} \right)\end{matrix}$

Another well known PSF in photography and in optics in general isblurring created by de-focusing. The different is that de-focusing canusually be depicted by a symmetrical Gaussian shift invariant PSF, whilemotion de-blurring is not.

The reason why motion de-blurring is not shift invariant is that theimage may not only shift but also rotate. Therefore, a completedescription of the motion blurring is an Affine transform that combinesshift and rotation based on the following transformation:

$\begin{matrix}{\begin{bmatrix}u \\v \\1\end{bmatrix} = \begin{bmatrix}{{Cos}\;\omega} & {{Sin}\;\omega} & {\Delta\; x} \\{{{- {Sin}}\;\omega}\;} & {\cos\;\omega} & {\Delta\; y} \\0 & 0 & 1\end{bmatrix}} & \left( {{Eq}.\mspace{14mu} 4} \right)\end{matrix}$

The PSF can be obtained empirically as part of a more generic field suchas system identification. For linear systems, the PSF can be determinedby obtaining the system's response to a known input and then solving theassociated inversion problems.

The known input can be for an optical system, a point, alsomathematically defined in the continuous world as a delta function δ(x),a line, an edge or a corner.

An example of a PSF can be found in many text books such as“Deconvolution of Images and Spectra” 2nd. Edition, Academic Press,1997, edited by Jannson, Peter A. and “Digital Image Restoration”,Prentice Hall, 1977 authored by Andrews, H. C. and Hunt, B. R.

The process of de-blurring an image is done using de-convolution whichis the mathematical form of separating between the convolve image andthe convolution kernel. However, as discussed in many publications suchas Chapter 1 of “Deconvolution of Images and Spectra” 2nd. Edition,Academic Press, 1997, edited by Jannson, Peter A., the problem ofde-convolution can be either unsolvable, ill-posed or ill-conditioned.Moreover, for a physical real life system, an attempt to find a solutionmay also be exacerbated in the presence of noise or sampling.

One may mathematically try and perform the restoration viade-convolution means without the knowledge of the kernel or in this casethe PSF. Such methods known also as blind de-convolution. The results ofsuch process with no a-priori knowledge of the PSF for a general opticalsystem are far from acceptable and require extensive computation.Solutions based on blind de-convolution may be found for specificcircumstances as described in “Automatic multidimensional deconvolution”J. Opt. Soc. Am. A, vol. 4(1), pp. 180-188, January 1987 to Lane et al,“Some Implications of Zero Sheets for Blind Deconvolution and PhaseRetrieval”, J. Optical Soc. Am. A, vol. 7, pp. 468-479, 1990 to Bates etal, Iterative blind deconvolution algorithm applied to phase retrieval”,J. Opt. Soc. Am. A, vol. 7(3), pp. 428-433, March 1990 to Seldin et aland “Deconvolution and Phase Retrieval With Use of Zero Sheets,” J.Optical Soc. Am. A, vol. 12, pp. 1,842-1,857, 1995 to Bones et al.However, as known to those familiar in the art of image restoration, andas explained in “Digital Image Restoration”, Prentice. Hall, 1977authored by Andrews, H. C. and Hunt, B. R., blurred images can besubstantially better restored when the blur function is known.

The article “Motion Deblurring Using Hybrid Imaging”, by Moshe Ben-Ezraand Shree K. Nayar, from the Proceedings IEEE Computer SocietyConference on Computer Vision and Pattern Recognition, 2003, determinesthe PSF of a blurred image by using a hybrid camera which takes a numberof relatively sharp reference images during the exposure period of themain image. However, this requires a special construction of camera andalso requires simultaneous capture of images. Thus this technique is notreadily transferable to cheap, mass-market digital cameras.

It is an object of the invention to provide an improved technique fordetermining a camera motion blur function in a captured digital imagewhich can take advantage of existing camera functionality and does nottherefore require special measurement hardware (although the use of theinvention in special or non-standard cameras is not ruled out).

SUMMARY OF THE INVENTION

According to the present invention there is provided a digital imageacquisition system comprising an apparatus for capturing digital imagesand a digital processing component for determining a camera motion blurfunction in a captured digital image based on a comparison of at leasttwo images each taken during, temporally proximate to or overlapping anexposure period of said captured image and of nominally the same scene.

Preferably, the at least two images comprise the captured image andanother image taken outside, preferably before and alternatively after,the exposure period of said captured image.

Preferably at least one reference image is a preview image.

Preferably, too, said digital image acquisition system is a portabledigital camera.

In one embodiment the digital processing component identifies at leastone characteristic in a single reference image which is relatively lessblurred than the corresponding feature in the captured image, andcalculates a point spread function (PSF) in respect of saidcharacteristic.

A characteristic as used in this invention may be a well-definedpattern. The better the pattern is differentiated from its surroundings,such as by local contrast gradient, local color gradient, well-definededges, etc., the better such pattern can be used to calculate the PSF.In an extreme case, the pattern forming the characteristic can be only asingle pixel in size.

In another embodiment the digital processing component calculates atrajectory of at least one characteristic in a plurality of referenceimages, extrapolates such characteristic on to the captured image, andcalculates a PSF in respect of said characteristic.

In either case, based on the calculated PSF, the captured image can bedeblurred using one of a number of de-convolution techniques known inthe art.

Corresponding de-blurring function determining methods are alsoprovided. One or more storage devices are also provided having digitalcode embedded thereon for programming one or more processors to performthe de-blurring function determining methods.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will now be described, by way of example,with reference to the accompanying drawings, in which:

FIG. 1 is a block diagram of a camera apparatus operating in accordancewith an embodiment of the present invention.

FIG. 2 illustrates the workflow of the initial stage of a camera motionblur reducing means using preview data according to embodiments of theinvention.

FIGS. 3-a to 3-c illustrate an example of a point spread function (PSF).

FIG. 4 is a workflow illustrating a first embodiment of the invention.

FIG. 5 is a workflow illustrating a second embodiment of the invention.

FIGS. 6 and 7- a and 7-b are diagrams which assist in the understandingof the second embodiment.

DESCRIPTION OF A PREFERRED EMBODIMENT

FIG. 1 shows a block diagram of an image acquisition system such as adigital camera apparatus operating in accordance with the presentinvention. The digital acquisition device, in this case a portabledigital camera 20, includes a processor 120. It can be appreciated thatmany of the processes implemented in the digital camera may beimplemented in or controlled by software operating in a microprocessor(pProc), central processing unit (CPU), controller, digital signalprocessor (DSP) and/or an application specific integrated circuit(ASIC), collectively depicted as block 120 and termed as “processor”.Generically, all user interface and control of peripheral componentssuch as buttons and display is controlled by a p-controller 122.

The processor 120, in response to a user input at 122, such as halfpressing a shutter button (pre-capture mode 32), initiates and controlsthe digital photographic process. Ambient light exposure is determinedusing light sensor 40 in order to automatically determine if a flash isto be used. The distance to the subject is determined using focusingmeans 50 which also focuses the image on image capture means 60. If aflash is to be used, processor 120 causes the flash means 70 to generatea photographic flash in substantial coincidence with the recording ofthe image by image capture means 60 upon full depression of the shutterbutton. The image capture means 60 digitally records the image incolour. The image capture means is known to those familiar with the artand may include a CCD (charge coupled device) or CMOS to facilitatedigital recording. The flash may be selectively generated either inresponse to the light sensor 40 or a manual input 72 from the user ofthe camera.

The image recorded by image capture means 60 is stored in image storemeans 80 which may comprise computer memory such a dynamic random accessmemory or a non-volatile memory. The camera is equipped with a display100, such as an LCD at the back of the camera or a microdisplay insidethe viewfinder, for preview and post-view of images. In the case ofpreview images, which are generated in the pre-capture mode 32, thedisplay 100 can assist the user in composing the image, as well as beingused to determine focusing and exposure. A temporary storage space 82 isused to store one or plurality of the preview images and be part of theimage store means 80 or a separate component. The preview image isusually generated by the same image capture means 60, and for speed andmemory efficiency reasons may be generated by subsampling the image 124using software which can be part of the general processor 120 ordedicated hardware, before displaying 100 or storing 82 the previewimage.

Upon full depression of the shutter button, a full resolution image isacquired and stored, 80. The image may go through image processingstages such as conversion from the RAW sensor pattern to RGB, format,color correction and image enhancements. These operations may beperformed as part of the main processor 120 or by using a secondaryprocessor such as a dedicated DSP. Upon completion of the imageprocessing the images are stored in a long term persistent storage suchas a removable storage device 112.

According to this embodiment, the system includes a motion de-blurringcomponent 100. This component can be implemented as firmware or softwarerunning on the main processor 120 or on a separate processor.Alternatively, this component may be implemented in software running onan external processing device 10, such as a desktop or a server, whichreceives the images from the camera storage 112 via the image outputmechanism 110, which can be physical removable storage, wireless ortethered connection between the camera and the external device. Themotion de-blurring component 100 includes a PSF calculator 110 and animage de-convolver 130 which de-convolves the full resolution imageusing the PSF. These two components may be combined or treatedseparately. The PSF calculator 110 may be used for qualification only,such as determining if motion blur exists, while the image de-convolver130 may be activated only after the PSF calculator 110 has determined ifde-blurring is needed.

FIG. 2 is a flow chart of one embodiment of calculating the PSF inaccordance with the present invention. While the camera is in previewmode, 210, the camera continuously acquires preview images, calculatingexposure and focus and displaying the composition. When such an imagesatisfies some predefined criteria 222, the preview image is saved, 230.As explained below, such criteria will be defined based on image qualityand/or chronological considerations. A simple criteria may be alwayssave the last image. More advanced image quality criteria may includeanalysis as to whether the preview image itself has too much motionblurring. As an alternative to saving a single image, multiple imagesmay be saved, 240, the newest preview image being added to the list,replacing the oldest one, 242 and 244. The definition of oldest can bechronological, as in First In First Out. Alternatively it can be theimage that least satisfies criteria as defined in stage 222. The processcontinues, 211, until the shutter release is fully pressed, 280, or thecamera is turned off.

The criteria, 222, that a preview image needs to satisfy can varydepending on specific implementations of the algorithm. In one preferredembodiment, such criteria may be whether the image is not blurred. Thisis based on the assumption that even if a camera is constantly moving,being hand held by the user, there are times where the movement is zero,whether because the user is firmly holding the camera or due to changeof movement direction the movement speed is zero at a certain instance.Such criteria may not need to be absolute. In addition such criteria maybe based on one or more 1-dimensional vectors as opposed to the full twodimensional image. In other words, the criteria 222 may be satisfied ifthe image is blurred horizontally, but no vertical movement is recordedand vice versa, due to the fact that the motion may be mathematicallydescribed in orthogonal vectors, thus separable. More straight forwardcriteria will be chronological, saving images every predefined timewhich can be equal or slower to the speed the preview images aregenerated. Other criteria may be defined such as related to theexposure, whether the preview reached focus, whether flash is beingused, etc.

Finally, the full resolution image acquired at 280 is saved, 282.

After the full resolution image is saved, 282, it is loaded into memory292 and the preview image or images are loaded into memory as well, 294.Together the preview and final images are the input of the process whichcalculates the PSF, 110.

A description of two different methods of calculating the PSF areillustrated in FIGS. 4 and 5.

FIG. 4 shows an embodiment 500 for extracting a PSF using a singlepreview image.

In this embodiment, the input is the finally acquired full resolutionimage 511, and a saved preview image 512. Prior to creating the PSF, thepreview and final image have to be aligned. The alignment can be aglobal operation, using the entire images, 511 and 512. However, the twoimages may not be exact for several reasons.

Due to the fact that the preview image and the final full resolutionimage differ temporally, there may not be a perfect alignment. In thiscase, local alignment, based on image features and using techniquesknown to those skilled in the art, will normally be sufficient. Theprocess of alignment may be performed on selected extracted regions 520,or as a local operation. Moreover, this alignment is only required inthe neighborhood of the selected region(s) or feature(s) used for thecreation of the PSF. In this case, matching regions of the fullresolution and preview image are extracted, 521 and 522. The process ofextraction of such regions may be as simple as separating the image intoa grid, which can be the entire image, or fine resolution regions. Othermore advanced schemes will include the detection of distinct regions ofinterest based on a classification process, such as detecting regionswith high contrast in color or exposure, sharp edges or otherdistinctive classifiers that will assist in isolating the PSF. Onefamiliar in the art is aware of many algorithms for analyzing anddetermining local features or regions of high contrast; frequencytransform and edge detection techniques are two specific examples thatmay be employed for this step, which may further include segmentation,feature extraction and classification steps.

The preview image 512 is normally, but not necessarily, of lowerresolution than the full resolution image 511, typically being generatedby clocking out a subset of the sensor cells or by averaging the rawsensor data. Therefore, the two images, or alternatively the selectedregions in the images, need to be matched in pixel resolution, 530. Inthe present context “pixel resolution” means the size of the image, orrelevant region, in terms of the number of pixels constituting the imageor region concerned. Such a process may be done by either upsampling thepreview image, 532, downsampling the acquired image, 531, or acombination thereof. Those familiar in the art will be aware of severaltechniques best used for such sampling methods.

Now we recall from before that:

-   -   A two dimensional image I is given as I(x,y).    -   A motion point spread function describing the blurring of image        I is given as MPSF(I).    -   The degraded image I′(x,y) can be mathematically defined as the        convolution of I(X,Y) and MPSF(x,y) or        I′(x,y)=I(x,y)        MPSF(x,y)  (Eq. 1)

Now it is well known that where a mathematical function, such as theaforementioned MPSF(x,y), is convoluted with a Dirac delta functionδ(x,y) that the original function is preserved. Thus, if within apreview image a sharp point against a homogenous background can bedetermined, it is equivalent to a local occurrence of a 2D Dirac deltafunction within the unblurred preview image. If this can now be matchedand aligned locally with the main, blurred image I′(x,y) then thedistortion pattern around this sharp point will be a very closeapproximation to the exact PSF which caused the blurring of the originalimage I(x,y). Thus, upon performing the alignment and resolutionmatching between preview and main images the distortion patternssurrounding distinct points or high contrast image features, are, ineffect, representations of the 2D PSF, for points and representation ofa single dimension of the PSF for sharp, unidirectional lines.

The PSF may be created by combining multiple regions. In the simplecase, a distinguished singular point on the preview image and itscorresponding motion blurred form of this point which is found in themain full-resolution image is the PSF.

However, as it may not always be possible to determine, match and align,a single distinct point in both preview and full resolution image, it isalternatively possible to create a PSF from a combination of theorthogonal parts of more complex features such as edges and lines.Extrapolation to multiple 1-D edges and corners should be clear for onefamiliar in the art. In this case multiple line-spread-functions,depicting the blur of orthogonal lines need to be combined and analysedmathematically in order to determine a single-point PSF.

Due to statistical variances this process may not be exact enough todistinguish the PSF based on a single region. Therefore, depending onthe processing power and required accuracy of the PSF, the step offinding the PSF may include some statistical pattern matching orstatistical combination of results from multiple regions within an imageto create higher pixel and potentially sub pixel accuracy for the PSF.

As explained above, the PSF may not be shift invariant. Therefore, theprocess of determining the right PSF may be performed in various regionsof the image, to determine the variability of the PSF as a function oflocation within the image.

FIG. 5 shows a method 600 of extrapolating a PSF using multiple previewimages.

In this embodiment, the movement of the image is extrapolated based onthe movement of the preview images. According to FIG. 5, the input forthis stage is multiple captured preview images 610, and the fullresolution image 620. All images are recorded with an exact time stampassociated with them to ensure the correct tracking. In most cases,preview images will be equally separated, in a manner of several imagesper second. However, this is not a requirement for this embodiment aslong as the interval between images, including the final full resolutionimage, is known.

One or more distinctive regions in a preview image are selected, 630. Bydistinctive, one refers to a region that can be isolated from thebackground, such as regions with noticeable difference in contrast orbrightness. Techniques for identifying such regions are well known inthe art and may include segmentation, feature extraction andclassification.

Each region is next matched with the corresponding region in eachpreview image, 632. In some cases not all regions may be accuratelydetermined on all preview images, due to motion blurring or objectobscurations, or the fact that they have moved outside the field of thepreview image: The coordinates of each region is recorded, 634, for thepreview images and, 636, for the final image.

Knowing the time intervals of the preview images, one can extrapolatethe movement of the camera as a function of time. When the fullresolution image 620 is acquired, the parameter that needs to berecorded is the time interval between the last captured preview imageand the full resolution image, as well as the duration of the exposureof the full resolution image. Based on the tracking before the image wascaptured, 634, and the interval before and duration of the final image,the movement of single points or high contrast image features can beextrapolated, 640, to determine the detailed motion path of the camera.

This process is illustrated in FIG. 6. According to this figure multiplepreview images 902, 904, 906, 908 are captured. In each of them aspecific region 912, 914, 916, 918 is isolated which corresponds to thesame feature in each image. The full resolution image is 910, and in itthe region corresponding to 912, 914, 916, 918 is marked as 920. Notethat 920 may be distorted due to motion blurring.

Tracking one dimension as a function of time, the same regions areillustrated in 930 where the regions are plotted based on theirdisplacement 932, as a function of time interval 932. The objects 942,944, 946 948 and 950 correspond to the regions 912, 914, 916, 918 and920.

The motion is calculated as the line 960. This can be done usingstatistical interpolation, spline or other curve interpolation based ondiscrete sampling points. For the final image, due to the fact that thecurve may not be possible to calculate, it may also be done viaextrapolation of the original curve, 960.

The region of the final acquired image is enlarged 970 for betterviewing. In this plot, the blurred object 950 is depicted as 952, andthe portion of the curve 690 is shown as 962. The time interval in thiscase, 935 is limited to the exact length in which the exposure is beingtaken, and the horizontal displacement 933, is the exact horizontalblur. Based on that, the interpolated curve, 952, within the exposuretime interval 935, produces an extrapolation of the motion path 990.

Now an extrapolation of the motion path may often be sufficient to yielda useful estimate of the PSF if the motion during the timeframe of theprinciple acquired image can be shown to have practically constantvelocity and practically zero acceleration. As many cameras nowincorporate sensitive gyroscopic sensors it may be feasible to determinesuch information and verify that a simple motion path analysis isadequate to estimate the motion blur PSF.

However when this is not the case (or where it is not possible toreliably make such a determination) it is still possible to estimate thedetailed motion blur PSF from a knowledge of the time separation andduration of preview images and a knowledge of the motion path of thecamera lens across an image scene. This process is illustrated in FIGS.7-a and 7-b and will now be described in more detail.

Any PSF is an energy distribution function which can be represented by aconvolution kernel k(x,y)→w where (x,y) is a location and w is theenergy level at that location. The kernel k must satisfy the followingenergy conservation constraint:∫∫k(x,y)dxdy=1,which states that energy is neither lost nor gained by the blurringoperation. In order to define additional constraints that apply tomotion blur PSFs we use a time parameterization of the PSF as a pathfunction, f(t)→(x,y) and an energy function h(t)→w. Note that due tophysical speed and acceleration constraints, f(t) should be continuousand at least twice differentiable, where f′(t) is the velocity of the(preview) image frame and f′(t) is the acceleration at time t. By makingthe assumption that the scene radiance does not change during imageacquisition, we get the additional constraint:

${{\int_{t}^{t + {\delta\; t}}{{h(t)}\ {\mathbb{d}t}}} = \frac{\delta\; t}{t_{end} - t_{start}}},{{\delta\; t} > 0},\;{t_{start} \leq t \leq {t_{end} - {\delta\; t}}},$where [t_(start), t_(end)] is the acquisition interval for a (preview)image. This constraint states that the amount of energy which isintegrated at any time interval is proportional to the length of theinterval.

Given these constraints we can estimate a continuous motion blur PSFfrom discrete motion samples as illustrated in FIGS. 7-a and 7-b. Firstwe estimate the motion path, f(t), by spline interpolation as previouslydescribed above and as illustrated in FIG. 6. This path [1005] isfurther illustrated in FIG. 7-a.

Now in order to estimate the energy function h(t) along this path weneed to determine the extent of each image frame along this interpolatedpath. This may be achieved using the motion centroid assumptiondescribed in Ben-Ezra et al and splitting the path into frames with a1-D Voronoi tessellation as shown in FIG. 7-a. Since the assumption ofconstant radiance implies that frames with equal exposure times willintegrate equal amounts of energy, we can compute h(t) for each frame asshown in FIG. 7-b. Note that as each preview frame will typically havethe same exposure time thus each rectangle in FIG. 7-b, apart from themain image acquisition rectangle will have equal areas. The area of themain image rectangle, associated with capture frame 5 [1020] Pin thisexample, will typically be several time larger than preview image framesand may be significantly more than an order of magnitude larger if theexposure time of the main image is long.

The resulting PSF determined by this process is illustrated in FIG. 7-band may be divided into several distinct parts. Firstly there is the PSFwhich is interpolated between the preview image frames [1052] and shownas a solid line; secondly there is the PSF interpolated between the lastpreview image and the midpoint of the main acquired image [1054];thirdly there is the extrapolation of the PSF beyond the midpoint of themain acquired image [1055] which, for a main image with a long exposuretime—and thus more susceptible to blurring—is more likely to deviatefrom the true PSF. Thus it may be desirable to acquire additionalpostview images, which are essentially images acquired through the samein-camera mechanism as preview images except that they are acquiredafter the main image has been acquired. This technique will allow afurther interpolation of the main image PSF [1056] with the PSFdetermined from at least one postview image.

The process may not be exact enough to distinguish the PSF based on asingle region. Therefore, depending on the processing power and accuracyneed, the step of finding the PSF may include some statistical patternmatching of multiple regions, determining multiple motion paths, thuscreating higher pixel and potentially sub pixel accuracy for the PSF.

Advantageously, a determination may be made whether a threshold amountof camera motion blur has occurred during the capture of a digitalimage. The determination is made based on a comparison of a least twoimages acquired during or proximate to the exposure period of thecaptured image. The processing occurs so rapidly, either in the cameraor in an external processing device, that the image blur determinationoccurs in “real time”. The photographer may be informed and/or a newimage capture can take place on the spot due to this real time imageblur determination feature. Preferably, the determination is made basedon a calculated camera motion blur function, and further preferably, theimage may be de-blurred based on the motion blur function, eitherin-camera or in an external processing device in real time or later on.

While an exemplary drawings and specific embodiments of the presentinvention have been described and illustrated, it is to be understoodthat that the scope of the present invention is not to be limited to theparticular embodiments discussed. Thus, the embodiments shall beregarded as illustrative rather than restrictive, and it should beunderstood that variations may be made in those embodiments by workersskilled in the arts without departing from the scope of the presentinvention as set forth in the appended claims and structural andfunctional equivalents thereof.

In addition, in methods that may be performed according to preferredembodiments herein and that may have been described above, theoperations have been described in selected typographical sequences.However, the sequences have been selected and so ordered fortypographical convenience and are not intended to imply any particularorder for performing the operations, except for those where a particularorder may be expressly set forth or where those of ordinary skill in theart may deem a particular order to be necessary.

In addition, all references cited herein as well as the background,invention summary, abstract and brief description of the drawings areincorporated by reference into the description of the preferredembodiment as disclosing alternative embodiments.

1. A method of extrapolating camera movement using multiple preview images, comprising: capturing multiple preview images and a full resolution image, each with a same sensor, such that at least a same portion of said same sensor captures at least a portion of each of said full resolution image and said multiple preview images, within a temporal range that includes an exposure period of the full resolution image, and times proximately before and after said exposure period, and of approximately the same scene as that of the full resolution image; recording said images with a time stamp; determining time intervals between images; selecting one or more distinctive regions within at least one of the preview images; matching corresponding regions within the preview images; recording coordinates of each of the corresponding regions; and extrapolating camera movement as a function of time based on the time intervals between images and the coordinates of the corresponding regions within the preview images.
 2. The method of claim 1, wherein the time intervals comprise the time interval between capture of the full resolution image and the last preview image.
 3. The method of claim 1, wherein the extrapolating of camera movement is further based on a duration of exposure of the full resolution image.
 4. The method of claim 1, wherein the extrapolating is based on tracking of camera movement before said capturing of the full resolution image.
 5. The method of claim 4, wherein the extrapolating is further based on the time interval between capture of the full resolution image and the last preview image.
 6. The method of claim 5, wherein the extrapolating is further based on a duration of exposure of the full resolution image.
 7. The method of claim 1, wherein the extrapolating of said camera movement comprises extrapolating movement of single points.
 8. The method of claim 1, wherein the extrapolating of camera movement comprises extrapolating movement of high contrast image features.
 9. The method of claim 1, wherein intervals between preview images are equally separated, in a manner of several images per second.
 10. The method of claim 1, wherein the selecting of distinctive regions comprises isolating a distinction region from background.
 11. The method of claim 10, wherein the distinction region comprises a noticeable difference in contrast or brightness, or both compared with the background.
 12. The method of claim 1, wherein the matching of corresponding regions within preview images comprises filtering images with intolerable motion blurring or object obscuration defects, or where a corresponding region has moved outside the field of the preview image, or combinations thereof.
 13. The method of claim 1, wherein the recording of coordinates comprises recording coordinates for preview images and for the full resolution image.
 14. The method of claim 1, further comprising determining distance of a subject using a focus component.
 15. The method of claim 1, further comprising determining whether the full resolution image comprises a threshold amount of motion blur based on the extrapolated camera movement.
 16. The method of claim 1, further comprising warning a photographer, capturing a new image or de-blurring the full resolution image based on the extrapolated camera movement.
 17. An image acquisition device configured to extrapolate image movement using multiple preview images, comprising: a processor; focus and image capture components configured to capture preview images and a full resolution image each with a same sensor, said components being configured such that at least a same portion of said same sensor captures at least a portion of each of said full resolution image and said multiple preview images, within a temporal range that includes an exposure period of the full resolution image, and times proximately before and after said exposure period, and of approximately the same scene as that of the full resolution image; a memory having code embedded therein for programming the processor to perform a method to extrapolate image movement using the preview images, wherein the method comprises: recording the multiple preview images and the full resolution image with a time stamp; determining time intervals between images; selecting one or more distinctive regions within at least one of the preview images; matching corresponding regions within the preview images; recording coordinates of each of the corresponding regions; and extrapolating camera movement as a function of time based on the time intervals between images and on the coordinates of the corresponding regions within the preview images.
 18. The device of claim 17, wherein the time intervals comprise the time interval between capture of the full resolution image and the last preview image.
 19. The device of claim 17, wherein the extrapolating of camera movement is further based on a duration of exposure of the full resolution image.
 20. The device of claim 17, wherein the extrapolating is based on tracking of camera movement before said capturing of the full resolution image.
 21. The device of claim 20, wherein the extrapolating is further based on the time interval between capture of the full resolution image and the last preview image.
 22. The device of claim 21, wherein the extrapolating is further based on a duration of exposure of the full resolution image.
 23. The device of claim 17, wherein the extrapolating of said camera movement comprises extrapolating movement of single points.
 24. The device of claim 17, wherein the extrapolating of camera movement comprises extrapolating movement of high contrast image features.
 25. The device of claim 17, wherein intervals between preview images are equally separated, in a manner of several images per second.
 26. The device of claim 17, wherein the selecting of distinctive regions comprises isolating a distinction region from background.
 27. The device of claim 26, wherein the distinction region comprises a noticeable difference in contrast or brightness, or both compared with the background.
 28. The device of claim 17, wherein the matching of corresponding regions within preview images comprises filtering images with intolerable motion blurring or object obscuration defects, or where a corresponding region has moved outside the field of the preview image, or combinations thereof.
 29. The device of claim 17, wherein the recording of coordinates comprises recording coordinates for preview images and for the full resolution image.
 30. The device of claim 17, wherein the focus component is configured to determine a distance of a subject within one or more of the preview images and the full resolution image.
 31. The device of claim 17, wherein the method further comprises determining whether the full resolution image comprises a threshold amount of motion blur based on the extrapolated camera movement.
 32. The device of claim 17, wherein the method further comprises warning a photographer, capturing a new image or de-blurring the full resolution image based on the extrapolated camera movement.
 33. A non-transitory memory having code embedded therein for programming a processor to perform a method to extrapolate image movement using preview images, wherein the method comprises: recording multiple preview images and a full resolution image with a time stamp, said multiple preview images and said full resolution images having been captured with a same digital image sensor, such that at least a same portion of said same sensor captures at least a portion of each of said full resolution image and said multiple preview images, within a temporal range that includes an exposure period of the full resolution image, and times proximately before and after said exposure period, and of approximately the same scene as that of the full resolution image; determining time intervals between images; selecting one or more distinctive regions within at least one of the preview images; matching corresponding regions within the preview images; recording coordinates of each of the corresponding regions; and extrapolating camera movement as a function of time based on the time intervals between images and on the coordinates of the corresponding regions within the preview images.
 34. The memory of claim 33, wherein the time intervals comprise the time interval between capture of the full resolution image and the last preview image.
 35. The memory of claim 33, wherein the extrapolating of camera movement is further based on a duration of exposure of the full resolution image.
 36. The memory of claim 33, wherein the extrapolating is based on tracking of camera movement before said capturing of the full resolution image.
 37. The memory of claim 36, wherein the extrapolating is further based on the time interval between capture of the full resolution image and the last preview image.
 38. The memory of claim 37, wherein the extrapolating is further based on a duration of exposure of the full resolution image.
 39. The memory of claim 33, wherein the extrapolating of said camera movement comprises extrapolating movement of single points.
 40. The memory of claim 33, wherein the extrapolating of camera movement comprises extrapolating movement of high contrast image features.
 41. The memory of claim 33, wherein intervals between preview images are equally separated, in a manner of several images per second.
 42. The memory of claim 33, wherein the selecting of distinctive regions comprises isolating a distinction region from background.
 43. The memory of claim 42, wherein the distinction region comprises a noticeable difference in contrast or brightness, or both compared with the background.
 44. The memory of claim 33, wherein the matching of corresponding regions within preview images comprises filtering images with intolerable motion blurring or object obscuration defects, or where a corresponding region has moved outside the field of the preview image, or combinations thereof.
 45. The memory of claim 33, wherein the recording of coordinates comprises recording coordinates for preview images and for the full resolution image.
 46. The memory of claim 33, wherein the focus component is configured to determine a distance of a subject within one or more of the preview images and the full resolution image.
 47. The memory of claim 33, wherein the method further comprises determining whether the full resolution image comprises a threshold amount of motion blur based on the extrapolated camera movement.
 48. The memory of claim 33, wherein the method further comprises warning a photographer, capturing a new image or de-blurring the full resolution image based on the extrapolated camera movement. 